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Detecting Alzheimer’s Disease Using Interactional and Acoustic Features from Spontaneous Speech

Shamila Nasreen, Julian Hough Orcid Logo, Matthew Purver

Interspeech 2021, Pages: 1962 - 1966

Swansea University Author: Julian Hough Orcid Logo

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DOI (Published version): 10.21437/interspeech.2021-1526

Abstract

Alzheimer’s Disease (AD) is a form of Dementia that manifests in cognitive decline including memory, language, and changes in behavior. Speech data has proven valuable for inferring cognitive status, used in many health assessment tasks, and can be easily elicited in natural settings. Much work focu...

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Published in: Interspeech 2021
ISBN: 9781713836902
ISSN: 2958-1796
Published: ISCA 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa64933
Abstract: Alzheimer’s Disease (AD) is a form of Dementia that manifests in cognitive decline including memory, language, and changes in behavior. Speech data has proven valuable for inferring cognitive status, used in many health assessment tasks, and can be easily elicited in natural settings. Much work focuses on analysis using linguistic features; here, we focus on non-linguistic features and their use in distinguishing AD patients from similar-age Non-AD patients with other health conditions in the Carolinas Conversation Collection (CCC) dataset. We used two types of features: patterns of interaction including pausing behaviour and floor control, and acoustic features including pitch, amplitude, energy, and cepstral coefficients. Fusion of the two kinds of features, combined with feature selection, obtains very promising classification results: classification accuracy of 90% using standard models such as support vector machines and logistic regression. We also obtain promising results using interactional features alone (87% accuracy), which can be easily extracted from natural conversations in daily life and thus have the potential for future implementation as a noninvasive method for AD diagnosis and monitoring.
College: Faculty of Science and Engineering
Funders: Purver is partially supported by the EPSRC under grant EP/S033564/1, and by the European Union’s Horizon 2020 programme under grant agreements 769661 (SAAM, Supporting Active Ageing through Multimodal coaching) and 825153 (EMBEDDIA, Cross-Lingual Embeddings for Less-Represented Languages in European News Media).
Start Page: 1962
End Page: 1966

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